Citations

Introduction: The effect of obesity on physiologic aging has not been completely established. We previously developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm that predicts age and determines that the difference between AI-ECG age and chronological age (Age-Gap) may reflect accelerated aging, as it is associated with increased total and cardiovascular (CV) mortality. Hypothesis: Obesity is associated with an increased Age-Gap reflecting morbidity and accelerated aging. Methods: We included adults undergoing a wellness evaluation including, body fat percentage (BF%) determination by air displacement plethysmography (Bod-Pod®) and a 12-lead ECG within 2 years. We excluded those with coronary artery disease, congestive heart failure, stroke or being underweight defined as having a BMI<18.5 kg/m2. Logistic regression models tested the association between obesity by BMI (≥30 kg/m2) or BF% quartiles (≥35% for men and ≥25 in women) and being older by AI-ECG. Results: We included 1295 subjects (70% women), mean chronological age 46.7 ± 13.1 years. Mean Age-gap positive difference compared to those with BMI<25 was 1.39, 2.06 and 2.83 for those with BMI 25-29.9, 30-34.9, and >35 respectively (p<0.05). In the BF% group, Age-gap difference compared to those in the 1st quartile was 0.008, 0.37 and 1.52 for those in the 2nd, 3rd, and 4th quartile (p<0.05). Those>3 years older by ECG were more likely to have obesity by BMI and BF%, see figure. Conclusions: Obesity, either by BMI or BF%, is associated with increased physiologic age as measured by AI-ECG, highlighting the importance of obesity as a health threat.

Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired, life-threatening, stem cell disorder that renders red blood cells vulnerable to complement-mediated hemolysis. This study describes the health-related quality of life (HRQoL) and symptom-specific functional impairment of patients with PNH in a real-world setting. Methods: US-based adults with PNH treated with a parenterally administered complement inhibitor (PACI) for ≥6 months completed an online, cross-sectional, observational survey; a subset of patients also participated in individual semi-structured qualitative interviews after completing the survey. The survey included the PROMIS® 29+2 Profile v2.1 (PROMIS 29+2) to measure HRQoL. The FACIT Fatigue, Neuro-QOL Item Bank v2.0 Cognitive Function Short Form, and PROMIS Item Bank v1.0 Dyspnea Functional Limitations 10a Short Form measured functional impairment associated with specific symptoms of PNH. For each patient with PNH who completed the online survey, up to 3 age- (+/- 3 years) and sex-matched adults without PNH also completed the survey. The HRQoL and functional impairment of the PNH sample was compared to that of general population (GP) sample using independent samples t-tests or Mann-Whitney tests. To further explore the experiences of patients with PNH, the association between HRQoL/functional impairment and fatigue severity was investigated. Patients with PNH were grouped according to their self-reported fatigue severity over the past 7 days (very mild or mild vs moderate, severe, or very severe [no patients reported no fatigue]). Differences in scores across these 2 groups were tested. Data from the qualitative interviews were coded using thematic analysis to explore key concepts related to HRQoL and impairment due to PNH symptoms. Data collection began in March 2023; results are based on interim data. Results: Demographic characteristics of the study samples (PNH survey sample: N=31, GP survey sample: N=62, PNH interview sample: N=10) are shown in Table 1. Patients were treated for PNH with eculizumab, pegcetacoplan, or ravulizumab, with an average duration of current PACI treatment of 3.4 years. Compared to the age and sex-matched GP sample, patients with PNH had significantly higher median scores on the PROMIS 29+2 fatigue domain (GP: 48.70 vs PNH: 57.10, p=0.041) and PROMIS Dyspnea Functional Limitations short form (GP: 40.70 vs PNH: 48.60, p=0.036), indicating that patients with PNH experience more fatigue and dyspnea limitations than the GP sample. Patients with PNH also had significantly lower median scores on the PROMIS 29+2 ability to participate in social roles/activities domain (GP: 53.50 vs PNH: 44.20, p=0.013), indicating greater role/activity limitations among patients with PNH. Compared to the GP sample, patients with PNH had numerically lower (i.e., worse) median PROMIS 29+2 cognitive functioning domain scores (GP: 50.00 vs PNH: 46.30, p=0.058) and FACIT Fatigue scores (GP: 39.50 vs PNH: 30.00, p=0.057). Within the PNH sample, statistically significant associations (p&lt;0.05) were observed between fatigue severity and HRQoL/functional impairment for all outcomes except for the PROMIS 29+2 sleep disturbance and cognitive functioning domains (Table 2). Patients with more severe fatigue had worse HRQoL and more functional impairment than patients with less severe fatigue. Interview participants described fatigue-related functional impairments in their physical, social, and cognitive functioning. Fatigue made daily activities (e.g., cooking, cleaning, going for a walk) difficult and sometimes impossible. Planning social activities in advance was also challenging as patients could not predict how they would feel on any given day, and often found they had to miss, postpone, or cancel social engagements. Finally, patients described experiencing “brain fog” including difficulty concentrating, forgetfulness, and delayed cognitive processing. Conclusions: Despite receiving treatment for PNH, patients experienced deficits in areas of HRQoL related to fatigue, participation in social roles, cognitive function, and functional limitations due to dyspnea, relative to a sample of GP adults. During follow-up interviews, patients emphasized the deep and broad impacts that fatigue and cognitive functioning had, making daily activities and their social lives very difficult.

Background There is limited prospective or comparative data evaluating the HRQoL impact of intensive chemotherapy (IC) in older adults (age ≥60 years) with AML. In the recent EA E2906 phase 3 study of '7&3' (daunorubicin & cytarabine) vs. clofarabine (CLO), a putative lower intensity therapy, we incorporated a prospective patient-reported outcomes (PRO) assessment of HRQoL and fatigue to understand treatment impact from the patients' (pts) perspective. Methods E2906 study design and results have been presented previously [n=727, med. age 68 years (range 60-86), randomized 1:1 to ‘standard’ ‘7&3‘ & high dose cytarabine (Arm A) vs. single agent CLO (Arm B), as remission induction (Step 1) and consolidation (Step 2), respectively]. There was no difference in composite complete remission (CCR, 50%) or 30-day mortality rates (8.5%), and in primary analysis CLO was inferior for Overall Survival. HRQoL was a key secondary protocol endpoint. PROs assessed HRQoL and leukemia-specific symptoms and concerns using Functional Assessment of Cancer Therapy-Leukemia scale (FACT-Leu), & Fatigue using FACT-Fatigue subscale. PROs were administered at prespecified time points: (1) Baseline; (2) Cycle 1 at nadir (approx. Day 15); (3) End of Step 1 restaging (approx. D 40); (4) Beginning of Step 2 (1 st consolidation, approx. D 45); and (5) 2 nd cycle consolidation/End Step 2 (approx. D 60). The FACT-Leu Trial Outcome Index ( TOI) was calculated by summing physical well-being, functional well-being, and leukemia-specific concerns subscales to create a single composite score. Higher TOI scores correspond to better HRQoL. The primary endpoint was to evaluate TOI difference from randomization to day 30 after induction therapy between arms, evaluated using Wilcoxon rank sum test. We also compared Clinically Meaningful differences (CMiD) in TOI (defined as patient change ≥½ standard deviation from baseline TOI) between arms, using the X2 test. Pts not completing at least 2 assessment forms (including baseline) were excluded. Testing of constructs of Physical Well-being (PWB), Functional, Leukemia & Fatigue scales demonstrated high Cronbach's a values (a &gt;0.7) except for PWB (a=0.672) at a single time point (End of Step 2). Results Baseline TOI was available for n=489, and 364 pts (n=182 each Arm) were evaluable in this planned analysis. Evaluating CMiD during Cycle 1 induction, pts in Arm B were less likely to experience ‘Worse’ TOI score (32% vs. 40.7%, Arm A), more likely to maintain stable TOI scores (49.1% vs. 39%, Arm A), and had a similar proportion of ‘Better’ TOI scores (20.3%, A vs. 18.9%, B) although differences were not significant (p=0.1389). Conversely in cycle 1 consolidation, more patients in Arm A experienced Better TOI (45.1% vs. 29.2%, Arm B), fewer had stable TOI (45.1% vs. 58.3%, Arm B), and rates of ‘Worse’ TOI were similar (9.8%, A vs 12.5%, Arm B), again differences were not significant (p=0.2616). There was no difference in Fatigue scores between Arms (p=0.49). We evaluated whether TOI and Fatigue scores could predict the ability to complete protocol therapy. In univariate analysis baseline TOI (p=0.0059) and Fatigue (p=0.0057) were associated with Step 1 completion, however on multivariate analysis (adjusting for age, sex, and Arm) were no longer significant [TOI, Odds Ratio (OR) 1.03 (0.98-1.08, p=0.27); Fatigue, OR 1.03 (0.96-1.11, p=0.42)]. There was a borderline association of TOI with Step 2 completion [TOI OR 1.10 (1.00-1.23, p=0.06)]. Using a multivariable linear mixed effects model, we observed similar and significant changes in TOI scores in both Arms, demonstrating the effect of IC & CLO on HRQoL (Figure). Comparing to baseline, TOI scores decreased significantly at C1 induction nadir (-4.0873, p&lt;0.0001), and increased by 5.0762 (p=0.0004) at end Step 1, by 6.9047 at cycle 1 consolidation (p&lt;0.0001), and by 11.4601 (p&lt;0.0001) at end Step 2. Differences were mainly apparent in those who achieved CCR, and there was significant attrition in PRO responses in non-CCR pts. Conclusions Older adults experienced significant improvement in HRQoL after nadir regardless of therapy, and those achieving CCR report significantly higher scores. We did not observe differences in leukemia specific HRQoL or fatigue between treatment Arms (7&3 vs. CLO). Patient-reported HRQoL & Fatigue was not predictive of completion of protocol therapy. These results serve as a benchmark for expectations with IC in older adults.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

AbstractBackgroundTobacco use is associated with adverse outcomes among patients diagnosed with cancer. Socioeconomic determinants influence access and utilization of tobacco treatment; little is known about the relationship between neighborhood socioeconomic disadvantage (NSD) and tobacco assessment, assistance, and cessation among patients diagnosed with cancer.MethodsA modified Cancer Patient Tobacco Use Questionnaire (C‐TUQ) was administered to patients enrolled in nine ECOG‐ACRIN clinical trials. We examined associations of NSD with (1) smoking status, (2) receiving tobacco cessation assessment and support, and (3) cessation behaviors. NSD was classified by tertiles of the Area Deprivation Index. Associations between NSD and tobacco variables were evaluated using logistic regression.ResultsA total of 740 patients completing the C‐TUQ were 70% male, 94% White, 3% Hispanic, mean age 58.8 years. Cancer diagnoses included leukemia 263 (36%), lymphoma 141 (19%), prostate 131 (18%), breast 79 (11%), melanoma 69 (9%), myeloma 53 (7%), and head and neck 4 (0.5%). A total of 402 (54%) never smoked, 257 (35%) had formerly smoked, and 81 (11%) were currently smoking. Patients in high disadvantaged neighborhoods were approximately four times more likely to report current smoking (odds ratio [OR], 3.57; 95% CI, 1.69–7.54; p = .0009), and more likely to report being asked about smoking (OR, 4.24; 95% CI, 1.64–10.98; p = .0029), but less likely to report receiving counseling (OR, 0.11; 95% CI, 0.02–0.58; p = .0086) versus those in the least disadvantaged neighborhoods.ConclusionsGreater neighborhood socioeconomic disadvantage was associated with smoking but less cessation support. Increased cessation support in cancer care is needed, particularly for patients from disadvantaged neighborhoods.

Abstract Background: While cigarette smoking has declined among the U.S. general population, sale and use of non-cigarette alternative tobacco products (ATP; e.g., e-cigarettes, cigars) and dual use of cigarettes/ATPs are rising. Little is known about ATP use patterns in cancer survivors enrolled in clinical trials. We investigated prevalence of tobacco product use, and factors associated with past 30-day use, among patients with cancer in national trials. Methods: Cancer survivors (N = 756) enrolled in 9 ECOG-ACRIN clinical trials (2017–2021) completed a modified Cancer Patient Tobacco Use Questionnaire (C-TUQ) which assessed baseline cigarette and ATP use since cancer diagnosis and in the past 30 days. Results: Patients were on average 59 years old, 70% male, and the mean time since cancer diagnosis was 26 months. Since diagnosis, cigarettes (21%) were the most common tobacco product used, followed by smokeless tobacco use (5%), cigars (4%), and e-cigarettes (2%). In the past 30 days, 12% of patients reported smoking cigarettes, 4% cigars, 4% using smokeless tobacco, and 2% e-cigarettes. Since cancer diagnosis, 5.5% of the sample reported multiple tobacco product use, and 3.0% reported multiple product use in the past 30 days. Males (vs. females; OR 4.33; P = 0 &lt; 0.01) and individuals not living with another person who smokes (vs. living with; OR, 8.07; P = 0 &lt; 0.01) were more likely to use ATPs only versus cigarettes only in the past 30 days. Conclusions: Among patients with cancer, cigarettes were the most prevalent tobacco product reported. Impact: Regardless, ATPs and multiple tobacco product use should be routinely assessed in cancer care settings.

AbstractSynucleinopathies‐related disorders such as Lewy body dementia (LBD) and isolated/idiopathic REM sleep behavior disorder (iRBD) have been associated with neuroinflammation. In this study, we examined whether the human leukocyte antigen (HLA) locus plays a role in iRBD and LBD. In iRBD, HLA‐DRB1*11:01 was the only allele passing FDR correction (OR = 1.57, 95% CI = 1.27–1.93, p = 2.70e‐05). We also discovered associations between iRBD and HLA‐DRB1 70D (OR = 1.26, 95%CI = 1.12–1.41, p = 8.76e‐05), 70Q (OR = 0.81, 95%CI = 0.72–0.91, p = 3.65e‐04) and 71R (OR = 1.21, 95%CI = 1.08–1.35, p = 1.35e‐03). Position 71 (pomnibus = 0.00102) and 70 (pomnibus = 0.00125) were associated with iRBD. Our results suggest that the HLA locus may have different roles across synucleinopathies.

Abstract Background: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.